Cyber Insurance Evolution: Addressing Ransomware and Supply Chain Risks

Authors

  • Komal Manohar Tekale Independent Researcher, USA. Author

DOI:

https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P113

Keywords:

Cyber Insurance, Ransomware, Supply Chain Risk, Cybersecurity, Systemic Risk, Actuarial Modeling, Risk Transfer, Monte Carlo Simulation

Abstract

The market of cyber insurance has considerably changed due to the rise of the prevalence of ransomware and supply chain cyberattacks. Cyber insurance, which was initially a niche financial tool, has since been a necessary risk transfer tool to businesses in the new digital ecosystem. The paper will focus on the history of cyber insurance with regards to two imminent risks, i.e., ransomware and the compromise of the supply chain. Ransomware attacks have become more numerous and sophisticated due to the presence of organized networks of cybercriminals as part of which they are carried out with the help of sophisticated encryption, affiliate business model and payment in cryptocurrency. Equally, the resiliency of supply chain risks has been characterized by the increasing dependency on third-party suppliers, cloud service providers and software dependencies, which weigh the system vulnerability. In this paper, I will begin by discussing the conceptual framework of cyber insurance, its application as a means of financial recovery and as an incentive to be more secure. The cyber insurance (unlike traditional one), will be forced to continuously incorporate itself into the changing technological environment, regulatory forces, and the constantly varied threat environment. In a bid to remain solvent, insurers are heading towards dynamic risk assessment, actuarial models and real time threat intelligence. The paper describes how ransomware has led to reshaping the policy architecture by insurers. Coverage limitations, higher premiums, exclusion, and proactive requirements such as mandatory multi-factor authentication (MFA), endpoint detection and response (EDR) systems have also been covered. Similarly, cases of breaches of the supply chain, the most notable, SolarWinds breach and Kasey ransomware attack, are demonstrations of the devastating character of systemic cyber risk. These incidents underscore the difficulty of the modelling of correlated risks when a single compromise is being experienced by thousands of insured entities. In some respects a literature review points us to how academia, industry and regulators have been in some combination affecting cyber insurance.

A literature of scholars has shown a flaw of inefficiency with the actuarial models due to lack of historical information and the uncertainty surrounding threat agents. Reportedly, according to industry readings, claims ratios are on the steep climb, and the application of exclusions is taken in respect of the cyber activities sponsored by the state.  The regulators have also focused on resilience, and insurers must change policy language and be financially solvent against accumulated losses. The approach that is advanced in this paper establishes a hybrid framework in responding ransomware and supply chain risks in cyber insurance. It combines actuarial modeling, threat intelligence that is qualitative and systemic risk simulation. Our Bayesian inference models are designed to estimate the probability of ransomware claims and Monte Carlo models are used to model dependencies in the supply chain. There is a multi-layered architecture outlined that connects security controls, design of insurance policies and evaluation of claims. The results are that the application of the proactive security requirements in cyber insurance policies is significant in reducing the overall claim rates. In addition, systemic supply chain risk models indicate that reinsurance system and risk sharing among the insurers should be formed in order to minimize the catastrophic exposures. Flowcharts and mathematical form are provided to show the interaction of insurance risks pools, insured companies and risk threats. As mentioned in the discussion the future of cyber insurance does not lie merely in indemnification but also in active co-operation in cyber risk management. Constant watch is becoming in their direction, and the artificial intelligence and the blockchain-based claims verification will be used. They arrive at the conclusion that in the event of improved integration of technology, harmonization of regulations and systemic resiliency modelling, cyber insurance will be a driving force of digital trust

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References

[1] Böhme, R., & Schwartz, G. (2010, June). Modeling cyber-insurance: towards a unifying framework. In WEIS.

[2] Karri, N. (2021). Self-Driving Databases. International Journal of Emerging Trends in Computer Science and Information Technology, 2(1), 74-83. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P10

[3] Biener, C., Eling, M., & Wirfs, J. H. (2015). Insurability of cyber risk: An empirical analysis. The Geneva Papers on Risk and Insurance-Issues and Practice, 40(1), 131-158.

[4] Romanosky, S. (2016). Examining the costs and causes of cyber incidents. Journal of Cybersecurity, 2(2), 121-135.

[5] Kharraz, A., Robertson, W., Balzarotti, D., Bilge, L., & Kirda, E. (2015, June). Cutting the gordian knot: A look under the hood of ransomware attacks. In International conference on detection of intrusions and malware, and vulnerability assessment (pp. 3-24). Cham: Springer International Publishing.

[6] Karri, N., Pedda Muntala, P. S. R., & Jangam, S. K. (2025). Predictive Performance Tuning. International Journal of Emerging Research in Engineering and Technology, 2(1), 67-76. https://doi.org/10.63282/3050-922X.IJERET-V2I1P108

[7] Dreyer, P., Jones, T., Klima, K., Oberholtzer, J., Strong, A., Welburn, J. W., & Winkelman, Z. (2018). Estimating the global cost of cyber risk. Research Reports RR-2299-WFHF, Rand Corporation.

[8] Regulation, P. (2018). General data protection regulation. Intouch, 25, 1-5.

[9] Karri, N. (2022). AI-Powered Anomaly Detection. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(2), 122-131. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I2P114

[10] Enjam, G. R. (2020). Ransomware Resilience and Recovery Planning for Insurance Infrastructure. International Journal of AI, BigData, Computational and Management Studies, 1(4), 29-37.

[11] Kshetri, N. (2020). The evolution of cyber-insurance industry and market: An institutional analysis. Telecommunications policy, 44(8), 102007.

[12] Kurmaiev, P., Seliverstova, L., Bondarenko, O., & Husarevych, N. (2020). Cyber insurance: the current situation and prospects of development. Amazonia Investiga, 9(28), 65-73.

[13] Karri, N., Pedda Muntala, P. S. R., & Jangam, S. K. (2022). Forecasting Hardware Failures or Resource Bottlenecks Before They Occur. International Journal of Emerging Research in Engineering and Technology, 3(2), 99-109. https://doi.org/10.63282/3050-922X.IJERET-V3I2P111

[14] Camillo, M. (2017). Cyber risk and the changing role of insurance. Journal of Cyber Policy, 2(1), 53-63.

[15] Robinson, A., Corcoran, C., & Waldo, J. (2022). New risks in ransomware: Supply chain attacks and cryptocurrency.

[16] Kenneally, E. (2021). Ransomware: a Darwinian opportunity for cyber insurance. Conn. Ins. LJ, 28, 165.

[17] Chockalingam, S., Pieters, W., Teixeira, A., & van Gelder, P. (2017, November). Bayesian network models in cyber security: a systematic review. In Nordic conference on secure IT systems (pp. 105-122). Cham: Springer International Publishing.

[18] Sommer, P., & Brown, I. (2011). Reducing systemic cybersecurity risk. Organisation for Economic Cooperation and Development Working Paper No. IFP/WKP/FGS (2011), 3.

[19] Karri, N. (2022). Predictive Maintenance for Database Systems. International Journal of Emerging Research in Engineering and Technology, 3(1), 105-115. https://doi.org/10.63282/3050-922X.IJERET-V3I1P111

Published

2023-10-30

Issue

Section

Articles

How to Cite

1.
Tekale KM. Cyber Insurance Evolution: Addressing Ransomware and Supply Chain Risks. IJETCSIT [Internet]. 2023 Oct. 30 [cited 2025 Oct. 26];4(3):124-33. Available from: https://www.ijetcsit.org/index.php/ijetcsit/article/view/410

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